(436 d 03:12 ago)
Posting: # 22883
Kindly look into the below request and give me your valuable suggestions
How to include the weight factor in statistical BE evaluation for adjusting weight to reflect group size differences in a single dose (equal dose in all healthy subjects) parallel design study conducted in different groups on different dates at the same clinical site, with large difference in the number of subjects in each group.
Edit: Category changed; see also this post #1. [Helmut]
(435 d 19:57 ago)
Posting: # 22884
As per EMA- In parallel design studies, the treatment groups should be comparable in all known variables that may affect the pharmacokinetics of the active substance (e.g. age, body weight, sex, ethnic origin, smoking status, extensive/poor metabolic status). This is an essential pre-requisite to give validity to the results from such studies.
In general, it is recommended to have balanced between treatment arms.
(435 d 03:30 ago)
Posting: # 22886
Hi Divyen & Kotu,
❝ As per EMA […]
@Divyen: You are absolutely right when it comes to designing a study.
@Kotu: Were you interested in what to do when Murphy’s law hit and it turned out that groups of eligible subjects differed by a great extent? If yes:
‘The precise model to be used for the analysis should be pre-specified in the protocol.’
If this was not the case, essentially you have two options.
Regulatory acceptance not guaranteed. At the 2nd GBHI conference (September 2016, Rockville) there was a discussion about adding body weight as a covariate in crossover studies in patients because it may change with time. Response of regulators: No (though apparently the FDA was more open to the idea).
❝ In general, it is recommended to have balanced between treatment arms.
Correct – even in a crossover. It is a common misconception that period effects mean out because T and R are affected to the same degree. That’s not correct for unbalanced sequences. However, unless the degree if imbalance is extreme, the bias is small.
Edit: The published Two-Stage-Design methods are also correct in the strict sense for balanced sequences only. At the end an -script where you can try to counteract imbalance by intentionally allocate subjects in the second stage in such a way that in the pooled analysis sequences are as balanced as possible.
Example: Potvin ‘Method B’ (default), 24 subjects dosed in the first stage, 12 eligible in sequence RT and 10 in sequence TR (dropout-rate ≈8.3%), CV 25%, exact sample size re-estimation (default) taking the stage-term in the pooled analysis into account.
Estimated n2 12. Assuming that we will see the same dropout-rate like in the first stage, adjusted n2 14. Instead of dosing seven subjects / sequence, we dose six in sequence RT and eight in sequence TR. If the dropout-rate is realized, we get an allocation-ratio of 1:0.9444, which is not that bad.
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
The quality of responses received is directly proportional to the quality of the question asked. 🚮
(435 d 03:18 ago)
Posting: # 22887
may I add that while adjustment for BW seems a nogo in 'ordinary' BE discplines, it is quite the opposite for biosimilars where you more often than not (said solely on basis of the biosimilars I am working on) add BW as a covariate in the model. I don't think it was entirely clear what type of product was behind the question in this case.
At any rate, regardless of whether BW is taken into account one way or another, all this should be established at the time of protocol drafting. If the idea to adjust by BW was a result of a failing BE study then, naturally, the prospects may not be good.
Pass or fail!